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Merged
dg845 merged 2 commits into from
Aug 13, 2026
Merged

Move LTX25AutoBlocks to new file with new model name #14453

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53f7e6d
Refactor LTX25AutoBlocks into its own file, give it its own model nam…
dg845 Aug 12, 2026
19d09e7
Merge branch 'main' into ltx-25-move-ltx25autoblocks
dg845 Aug 12, 2026
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4 changes: 2 additions & 2 deletions src/diffusers/modular_pipelines/ltx2/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -27,8 +27,8 @@
"LTX2ConditionBlocks",
"LTX2ImageToVideoBlocks",
"LTX2InContextBlocks",
"LTX25AutoBlocks",
]
_import_structure["modular_blocks_ltx25"] = ["LTX25AutoBlocks"]
_import_structure["modular_pipeline"] = ["LTX2ModularPipeline", "LTX25ModularPipeline"]

if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
Expand All @@ -44,8 +44,8 @@
LTX2ConditionBlocks,
LTX2ImageToVideoBlocks,
LTX2InContextBlocks,
LTX25AutoBlocks,
)
from .modular_blocks_ltx25 import LTX25AutoBlocks
from .modular_pipeline import LTX2ModularPipeline, LTX25ModularPipeline
else:
import sys
Expand Down
228 changes: 0 additions & 228 deletions src/diffusers/modular_pipelines/ltx2/modular_blocks_ltx2.py
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Original file line number Diff line number Diff line change
Expand Up @@ -33,7 +33,6 @@
)
from .decoders import (
LTX2AudioDecoderStep,
LTX2DiffusionVaeDecoderStep,
LTX2TrimConditionTokensStep,
LTX2VaeDecoderStep,
)
Expand Down Expand Up @@ -1893,230 +1892,3 @@ def outputs(self):
OutputParam.template("videos"),
OutputParam("audio", type_hint=torch.Tensor, description="The generated audio waveform."),
]


# auto_docstring
class LTX25DecoderStep(SequentialPipelineBlocks):
"""
Decode stage for LTX-2.5: denoises the video latents with the diffusion decoder and vocodes the audio latents (or
returns latents).

Components:
diffusion_decoder (`LTX2VideoDiffusionDecoderModel`) video_processor (`VideoProcessor`) audio_vae
(`AutoencoderKLLTX2Audio`) vocoder (`LTX2Vocoder`)

Inputs:
latents (`Tensor`):
Pre-generated noisy latents for image generation.
output_type (`str`, *optional*, defaults to pil):
Output format: 'pil', 'np', 'pt'.
height (`int`, *optional*, defaults to 512):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to 704):
The width in pixels of the generated image.
num_frames (`int`, *optional*, defaults to 121):
The number of frames in the generated video.
generator (`Generator`, *optional*):
Torch generator for deterministic generation.
dtype (`dtype`):
The dtype of the model inputs, can be generated in input step.
audio_latents (`Tensor`):
Denoised audio latents.
audio_num_frames (`int`):
Number of audio latent frames, used to unpack the audio latent sequence.

Outputs:
videos (`list`):
The generated videos.
audio (`Tensor`):
The generated audio waveform.
"""

model_name = "ltx2"
block_classes = [LTX2DiffusionVaeDecoderStep, LTX2AudioDecoderStep]
block_names = ["video_decode", "audio_decode"]

@property
def description(self):
return (
"Decode stage for LTX-2.5: denoises the video latents with the diffusion decoder and vocodes the audio "
"latents (or returns latents)."
)

@property
def outputs(self):
return [
OutputParam.template("videos"),
OutputParam("audio", type_hint=torch.Tensor, description="The generated audio waveform."),
]


# auto_docstring
class LTX25ConditionDecoderStep(SequentialPipelineBlocks):
"""
Decode stage for LTX-2.5 condition workflows: drops the appended keyframe-condition tokens, then denoises the video
latents with the diffusion decoder and vocodes the audio latents (or returns latents).

Components:
diffusion_decoder (`LTX2VideoDiffusionDecoderModel`) video_processor (`VideoProcessor`) audio_vae
(`AutoencoderKLLTX2Audio`) vocoder (`LTX2Vocoder`)

Inputs:
latents (`Tensor`):
Pre-generated noisy latents for image generation.
base_token_count (`int`):
Number of generated-video tokens, i.e. the sequence length before appended tokens.
output_type (`str`, *optional*, defaults to pil):
Output format: 'pil', 'np', 'pt'.
height (`int`, *optional*, defaults to 512):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to 704):
The width in pixels of the generated image.
num_frames (`int`, *optional*, defaults to 121):
The number of frames in the generated video.
generator (`Generator`, *optional*):
Torch generator for deterministic generation.
dtype (`dtype`):
The dtype of the model inputs, can be generated in input step.
audio_latents (`Tensor`):
Denoised audio latents.
audio_num_frames (`int`):
Number of audio latent frames, used to unpack the audio latent sequence.

Outputs:
videos (`list`):
The generated videos.
audio (`Tensor`):
The generated audio waveform.
"""

model_name = "ltx2"
block_classes = [LTX2TrimConditionTokensStep, LTX2DiffusionVaeDecoderStep, LTX2AudioDecoderStep]
block_names = ["trim_condition_tokens", "video_decode", "audio_decode"]

@property
def description(self):
return (
"Decode stage for LTX-2.5 condition workflows: drops the appended keyframe-condition tokens, then "
"denoises the video latents with the diffusion decoder and vocodes the audio latents (or returns "
"latents)."
)

@property
def outputs(self):
return [
OutputParam.template("videos"),
OutputParam("audio", type_hint=torch.Tensor, description="The generated audio waveform."),
]


# auto_docstring
class LTX25AutoDecoderStep(AutoPipelineBlocks):
"""
Auto decode block for LTX-2.5 that selects the decoder based on inputs.
- `LTX25ConditionDecoderStep` when `base_token_count` is present, i.e. the denoised sequence carries appended
keyframe / reference tokens (condition, in-context).
- `LTX25DecoderStep` otherwise (text-to-video, image-to-video).

Components:
diffusion_decoder (`LTX2VideoDiffusionDecoderModel`) video_processor (`VideoProcessor`) audio_vae
(`AutoencoderKLLTX2Audio`) vocoder (`LTX2Vocoder`)

Inputs:
latents (`Tensor`):
Pre-generated noisy latents for image generation.
base_token_count (`int`, *optional*):
Number of generated-video tokens, i.e. the sequence length before appended tokens.
output_type (`str`, *optional*, defaults to pil):
Output format: 'pil', 'np', 'pt'.
height (`int`, *optional*, defaults to 512):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to 704):
The width in pixels of the generated image.
num_frames (`int`, *optional*, defaults to 121):
The number of frames in the generated video.
generator (`Generator`, *optional*):
Torch generator for deterministic generation.
dtype (`dtype`):
The dtype of the model inputs, can be generated in input step.
audio_latents (`Tensor`):
Denoised audio latents.
audio_num_frames (`int`):
Number of audio latent frames, used to unpack the audio latent sequence.

Outputs:
videos (`list`):
The generated videos.
audio (`Tensor`):
The generated audio waveform.
"""

model_name = "ltx2"
# Mirrors `LTX2AutoDecoderStep` on the same `base_token_count` trigger; only the video decoder differs.
block_classes = [LTX25ConditionDecoderStep, LTX25DecoderStep]
block_names = ["condition", "default"]
block_trigger_inputs = ["base_token_count", None]

@property
def description(self):
return (
"Auto decode block for LTX-2.5 that selects the decoder based on inputs.\n"
" - `LTX25ConditionDecoderStep` when `base_token_count` is present, i.e. the denoised sequence carries "
"appended keyframe / reference tokens (condition, in-context).\n"
" - `LTX25DecoderStep` otherwise (text-to-video, image-to-video)."
)

@property
def outputs(self):
return [
OutputParam.template("videos"),
OutputParam("audio", type_hint=torch.Tensor, description="The generated audio waveform."),
]


class LTX25AutoBlocks(LTX2AutoBlocks):
"""
Auto blocks for LTX-2.5, supporting text-to-video, image-to-video, condition-to-video and in-context (IC-LoRA)
generation (joint video + audio).

Supported workflows:
- `text2video`: requires `prompt`
- `image2video`: requires `image`, `prompt`
- `condition`: requires `conditions`, `prompt`
- `in_context`: requires `reference_conditions`, `num_frames`, `prompt`
"""

model_name = "ltx2"
# Both lists are restated rather than inherited: `block_classes` and `block_names` are positional, so a subclass
# that overrides one and inherits the other silently desynchronizes when the parent's graph changes. Keep these in
# step with `LTX2AutoBlocks`; only the `decode` entry differs.
block_classes = [
LTX2AutoPromptEnhancerStep,
LTX2TextConditioningStep,
LTX2AutoDurationStep,
LTX2AutoVaeEncoderStep,
LTX2AutoConditionEncoderStep,
LTX2AutoReferenceEncoderStep,
LTX2AutoCoreDenoiseStep,
LTX25AutoDecoderStep,
]
block_names = [
"prompt_enhancer",
"text_encoder",
"duration",
"vae_encoder",
"condition_encoder",
"reference_encoder",
"denoise",
"decode",
]

@property
def description(self):
return (
"Auto blocks for LTX-2.5 supporting text-to-video, image-to-video, condition-to-video and in-context "
"(IC-LoRA) generation (joint video + audio). Identical to `LTX2AutoBlocks` except that the video decoder "
"is `LTX2DiffusionVaeDecoderStep`, since the diffusion decoder is the native default from LTX-2.5 on. To "
'decode with the convolutional VAE instead, swap the decode block: `blocks.sub_blocks["decode"] = '
"LTX2AutoDecoderStep()`."
)
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